Kotthoff L., Nanni M., Guidotti R., Òsullivan B.
Individual Mobility Profiles Constraint Programming Clustering Trajectories
Mobility profile mining is a data mining task that can be formulated as clustering over movement trajectory data. The main challenge is to separate the signal from the noise, i.e. one-off trips. We show that standard data mining approaches suffer the important drawback that they cannot take the symmetry of non-noise trajectories into account. That is, if a trajectory has a symmetric equivalent that covers the same trip in the reverse direction, it should become more likely that neither of them is labelled as noise. We present a constraint model that takes this knowledge into account to produce better clusters. We show the efficacy of our approach on real-world data that was previously processed using standard data mining techniques.
Source: Principles and Practice of Constraint Programming. 21st International Conference, pp. 638–653, Cork, Ireland, 31/09/2015-04/10/2015
Publisher: Springer, Berlin , Germania
@inproceedings{oai:it.cnr:prodotti:345109, title = {Find your way back: Mobility profile mining with constraints}, author = {Kotthoff L. and Nanni M. and Guidotti R. and Òsullivan B.}, publisher = {Springer, Berlin , Germania}, doi = {10.1007/978-3-319-23219-5_44}, booktitle = {Principles and Practice of Constraint Programming. 21st International Conference, pp. 638–653, Cork, Ireland, 31/09/2015-04/10/2015}, year = {2015} }